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Dayo Bamikole
This course will teach you about the different types of Data Normalization techniques you can use when preparing your Deep Learning datasets in Machine Learning.
Read more
This course will teach you about the different types of Data Normalization techniques you can use when preparing your Deep Learning datasets in Machine Learning.
Read more
This course will teach you about the different types of Data Normalization techniques you can use when preparing your Deep Learning datasets in Machine Learning.
Deep Learning is a branch of Machine Learning based on Neural Networks. In this course, Literacy Essentials: Core Concepts Data Normalization, you’ll learn to use Normalization to place your dataset in an even playing field such that it can be easily analyzed. First, you’ll gain an understanding of why Normalization is needed in Deep Learning and in Machine Learning as a whole. Next, you’ll discover the different Normalization techniques available in Deep Learning. Finally, you’ll learn how to Implement these techniques by taking a look at scenarios. When you’re finished with this course, you’ll have the skills and knowledge of the Core Concepts of Data Normalization needed to understand Deep Learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/uskafa/java
What's inside
Syllabus
Course Overview
Evaluate Normalization Techniques for Deep Learning
Understand Types of Normalization
Case Study on Appropriate Normalization Technique
Read more
Syllabus
Course Overview
Evaluate Normalization Techniques for Deep Learning
Understand Types of Normalization
Case Study on Appropriate Normalization Technique
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores normalization techniques needed for machine learning and deep learning
Covers different types of normalization methods
Provides scenarios to illustrate how to implement normalization techniques
Taught by Dayo Bamikole, an expert in data normalization
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Java SE 8: Building Your First JavaFX Application with these
activities:
Connect with experts in data normalization
Show steps
Expand your network by reaching out to experts in the field of data normalization to gain valuable insights and guidance.
Browse courses on
Data Normalization
Show steps
-
Identify potential mentors through online platforms, conferences, or research papers.
-
Craft a personalized message expressing your interest in their expertise.
-
Schedule a meeting or engage in online discussions to learn from their experience and perspectives.
Review core concepts of Deep Learning
Show steps
Refresh your understanding of key concepts in Deep Learning, such as neural networks and machine learning algorithms, to strengthen your foundation for this course.
Browse courses on
Deep Learning
Show steps
-
Revisit introductory materials on Deep Learning, such as online tutorials or textbooks.
-
Go over your notes or previous coursework related to Deep Learning.
-
Complete practice questions or exercises to test your understanding.
Discuss normalization strategies with peers
Show steps
Deepen your comprehension by engaging in discussions with peers, sharing insights and experiences related to normalization strategies.
Browse courses on
Normalization Techniques
Show steps
-
Join or create a study group or online forum focused on Deep Learning.
-
Initiate discussions on various normalization techniques and their applications.
-
Share your own experiences and learn from the perspectives of others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Normalize datasets using different techniques
Show steps
Apply various normalization techniques to sample datasets and observe the impact on model performance.
Browse courses on
Data Normalization
Show steps
-
Collect or generate a set of sample datasets.
-
Implement different normalization techniques, such as min-max normalization, z-score normalization, and decimal scaling.
-
Train and evaluate machine learning models using the normalized datasets.
-
Compare the performance of models trained on normalized and unnormalized datasets.
Create a data normalization cheat sheet
Show steps
Solidify your understanding by creating a comprehensive cheat sheet summarizing key normalization techniques, their applications, and advantages.
Browse courses on
Normalization Techniques
Show steps
-
Gather information on different normalization techniques.
-
Organize and present the information in a clear and concise format.
-
Share your cheat sheet with others for feedback and discussion.
Attend a workshop on advanced normalization techniques
Show steps
Gain specialized knowledge by attending a workshop led by industry professionals who can provide hands-on training and practical insights.
Browse courses on
Normalization Techniques
Show steps
-
Research and identify workshops focused on advanced normalization techniques.
-
Register for and attend the workshop.
-
Actively participate in discussions and ask questions to enhance your understanding.
-
Follow up with the instructors or organizers for additional resources and support.
Explore advanced normalization techniques
Show steps
Expand your knowledge by exploring advanced normalization techniques beyond those covered in the course, such as batch normalization or layer normalization.
Browse courses on
Normalization Techniques
Show steps
-
Identify advanced normalization techniques used in Deep Learning research.
-
Locate online tutorials or documentation explaining these techniques.
-
Follow the tutorials to implement and experiment with the techniques.
-
Evaluate the effectiveness of these techniques compared to standard normalization methods.
Build a data normalization pipeline
Show steps
Enhance your practical skills by creating a data normalization pipeline that can be applied to real-world datasets.
Browse courses on
Data Normalization
Show steps
-
Design the architecture of the pipeline, including data sources, preprocessing steps, and normalization methods.
-
Implement the pipeline using appropriate programming tools and libraries.
-
Test and evaluate the pipeline's performance on different datasets.
-
Document and present your pipeline for potential use by others.
Connect with experts in data normalization
Show steps
Expand your network by reaching out to experts in the field of data normalization to gain valuable insights and guidance.
Browse courses on
Data Normalization
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms, conferences, or research papers.
- Craft a personalized message expressing your interest in their expertise.
- Schedule a meeting or engage in online discussions to learn from their experience and perspectives.
Review core concepts of Deep Learning
Show steps
Refresh your understanding of key concepts in Deep Learning, such as neural networks and machine learning algorithms, to strengthen your foundation for this course.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Revisit introductory materials on Deep Learning, such as online tutorials or textbooks.
- Go over your notes or previous coursework related to Deep Learning.
- Complete practice questions or exercises to test your understanding.
Discuss normalization strategies with peers
Show steps
Deepen your comprehension by engaging in discussions with peers, sharing insights and experiences related to normalization strategies.
Browse courses on
Normalization Techniques
Show steps
Show steps
Show steps
- Join or create a study group or online forum focused on Deep Learning.
- Initiate discussions on various normalization techniques and their applications.
- Share your own experiences and learn from the perspectives of others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Normalize datasets using different techniques
Show steps
Apply various normalization techniques to sample datasets and observe the impact on model performance.
Browse courses on
Data Normalization
Show steps
Show steps
Show steps
- Collect or generate a set of sample datasets.
- Implement different normalization techniques, such as min-max normalization, z-score normalization, and decimal scaling.
- Train and evaluate machine learning models using the normalized datasets.
- Compare the performance of models trained on normalized and unnormalized datasets.
Create a data normalization cheat sheet
Show steps
Solidify your understanding by creating a comprehensive cheat sheet summarizing key normalization techniques, their applications, and advantages.
Browse courses on
Normalization Techniques
Show steps
Show steps
Show steps
- Gather information on different normalization techniques.
- Organize and present the information in a clear and concise format.
- Share your cheat sheet with others for feedback and discussion.
Attend a workshop on advanced normalization techniques
Show steps
Gain specialized knowledge by attending a workshop led by industry professionals who can provide hands-on training and practical insights.
Browse courses on
Normalization Techniques
Show steps
Show steps
Show steps
- Research and identify workshops focused on advanced normalization techniques.
- Register for and attend the workshop.
- Actively participate in discussions and ask questions to enhance your understanding.
- Follow up with the instructors or organizers for additional resources and support.
Explore advanced normalization techniques
Show steps
Expand your knowledge by exploring advanced normalization techniques beyond those covered in the course, such as batch normalization or layer normalization.
Browse courses on
Normalization Techniques
Show steps
Show steps
Show steps
- Identify advanced normalization techniques used in Deep Learning research.
- Locate online tutorials or documentation explaining these techniques.
- Follow the tutorials to implement and experiment with the techniques.
- Evaluate the effectiveness of these techniques compared to standard normalization methods.
Build a data normalization pipeline
Show steps
Enhance your practical skills by creating a data normalization pipeline that can be applied to real-world datasets.
Browse courses on
Data Normalization
Show steps
Show steps
Show steps
- Design the architecture of the pipeline, including data sources, preprocessing steps, and normalization methods.
- Implement the pipeline using appropriate programming tools and libraries.
- Test and evaluate the pipeline's performance on different datasets.
- Document and present your pipeline for potential use by others.
Career center
Learners who complete Java SE 8: Building Your First JavaFX Application will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses scientific methods and processes to transform raw data into organized, meaningful information. They are responsible for gathering, cleaning, and analyzing large datasets, and then presenting the results in a clear and concise manner. This course will provide you with the essential skills and knowledge you need to develop and implement data normalization techniques, enabling you to improve the accuracy and efficiency of your data analysis, which is crucial for a successful Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models and systems. They work closely with data scientists and software engineers to ensure that machine learning models are implemented and deployed in a way that meets the needs of the business. This course will teach you about the different types of data normalization techniques you can use when preparing your Deep Learning datasets, which is essential for building effective machine learning models.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, interprets, and presents data to help businesses make informed decisions. They use data visualization techniques to communicate complex data in a clear and concise manner. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your data analysis.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to help businesses make better decisions. They analyze data to identify trends and patterns, and then develop recommendations for how to improve business operations. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your data analysis.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and infrastructure. They work closely with data scientists and data analysts to ensure that data is stored, processed, and analyzed in a way that meets the needs of the business. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your data pipelines.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work closely with other engineers and stakeholders to ensure that software applications meet the needs of the business. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your software applications.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data and make predictions about the future. They work in a variety of industries, including finance, insurance, and healthcare. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your quantitative models.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. They work in a variety of industries, including healthcare, education, and government. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your statistical analysis.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve problems and improve decision-making. They work in a variety of industries, including manufacturing, transportation, and healthcare. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your operations research models.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand consumer behavior and market trends. They use this information to help businesses make informed decisions about product development, marketing, and pricing. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your market research.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make recommendations about investments and financial decisions. They work in a variety of industries, including banking, insurance, and investment management. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your financial analysis.
Actuary
Actuary
An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your actuarial models.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist uses data visualization techniques to communicate complex data in a clear and concise manner. They work closely with data scientists and data analysts to develop data visualizations that meet the needs of the business. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your data visualizations.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and systems to identify opportunities for improvement. They work closely with stakeholders to develop and implement solutions that meet the needs of the business. This course will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which is essential for ensuring the accuracy and reliability of your business analysis.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. They work closely with stakeholders to ensure that projects are completed on time, within budget, and to the required quality standards. This course may be useful for Project Managers who are working on projects that involve data analysis or data management. It will provide you with the skills and knowledge you need to understand and apply data normalization techniques, which can help you to ensure the accuracy and reliability of your project data.
For more career information including salaries, visit:
OpenCourser.com/course/uskafa/java
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Java SE 8: Building Your First JavaFX Application.
Provides a comprehensive overview of the Java SE 8 platform, covering everything from basic syntax to advanced topics such as lambdas and streams. It valuable reference for both beginners and experienced Java programmers.
Comprehensive and up-to-date reference for Java programmers. It covers all the latest features of Java SE 11, including modules, local-variable type inference, and the new switch expression.
Save
Provides a comprehensive introduction to JavaFX 8, the latest version of Java's GUI toolkit. It covers everything from basic concepts to advanced topics such as 3D graphics and animation.
Comprehensive and up-to-date reference for JavaFX 8 developers. It covers all the latest features of JavaFX 8, including the new Modena theme and the enhanced Chart API.
Save
Fun and engaging introduction to Java programming. It uses a conversational style and plenty of examples to make learning Java easy and enjoyable.
Save
Collection of practical examples for creating animations in JavaFX. It covers a wide range of topics, from basic animations to complex 3D effects.
Provides a comprehensive guide to developing games with JavaFX. It covers everything from creating a basic game to working with advanced features such as physics and artificial intelligence.
Save
Practical guide to building JavaFX applications. It covers all the essential topics, from creating a basic user interface to working with advanced features such as 3D graphics and animation.
Save
Collection of recipes for solving common problems in JavaFX development. It covers a wide range of topics, from creating custom controls to working with data.
Beginner-friendly introduction to Java programming. It covers the basics of Java syntax and programming concepts, and it includes plenty of examples and exercises.
Save
Beginner-friendly introduction to JavaFX. It covers the basics of JavaFX syntax and programming concepts, and it includes plenty of examples and exercises.
Save
Friendly and accessible introduction to Java programming. It covers the basics of Java syntax and programming concepts, and it includes plenty of examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uskafa/java
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Time
123 hours
Level
Introductory
Via
Pluralsight
Instructor
Dayo Bamikole
Language
English
Good to know
Explores normalization techniques needed for machine learning and deep learning
Covers different types of normalization methods
Provides scenarios to illustrate how to implement normalization techniques
Taught by Dayo Bamikole, an expert in data normalization
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